Accelerating neuromorphic vision algorithms for recognition

Ahmed Al Maashri*, Michael DeBole, Matthew Cotter, Nandhini Chandramoorthy, Yang Xiao, Vijaykrishnan Narayanan, Chaitali Chakrabarti

*المؤلف المقابل لهذا العمل

نتاج البحث: Conference contribution

42 اقتباسات (Scopus)


Video analytics introduce new levels of intelligence to automated scene understanding. Neuromorphic algorithms, such as HMAX, are proposed as robust and accurate algorithms that mimic the processing in the visual cortex of the brain. HMAX, for instance, is a versatile algorithm that can be repurposed to target several visual recognition applications. This paper presents the design and evaluation of hardware accelerators for extracting visual features for universal recognition. The recognition applications include object recognition, face identification, facial expression recognition, and action recognition. These accelerators were validated on a multi-FPGA platform and significant performance enhancement and power efficiencies were demonstrated when compared to CMP and GPU platforms. Results demonstrate as much as 7.6X speedup and 12.8X more power-efficient performance when compared to those platforms.

اللغة الأصليةEnglish
عنوان منشور المضيفProceedings of the 49th Annual Design Automation Conference, DAC '12
عدد الصفحات6
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2012
الحدث49th Annual Design Automation Conference, DAC '12 - San Francisco, CA, United States
المدة: يونيو ٣ ٢٠١٢يونيو ٧ ٢٠١٢

سلسلة المنشورات

الاسمProceedings - Design Automation Conference
رقم المعيار الدولي للدوريات (المطبوع)0738-100X


Other49th Annual Design Automation Conference, DAC '12
الدولة/الإقليمUnited States
المدينةSan Francisco, CA

ASJC Scopus subject areas

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